Charmonium spectral functions from 2+1 flavour lattice QCD

Finite temperature charmonium spectral functions in the pseudoscalar and vector channels are studied in lattice QCD with 2+1 flavours of dynamical Wilson quarks, on fine isotropic lattices (with a lattice spacing of 0.057 fm), with a non-physical pion mass of $m_{\pi} \approx$ 545 MeV. The highest temperature studied is approximately $1.4 T_c$. Up to this temperature no significant variation of the spectral function is seen in the pseudoscalar channel. The vector channel shows some temperature dependence, which seems to be consistent with a temperature dependent low frequency peak related to heavy quark transport, plus a temperature independent term at \omega>0. These results are in accord with previous calculations using the quenched approximation.Comment: 17 pages, 9 figures, 2 table

QCD thermodynamics with Wilson fermions

QCD is investigated at finite temperature using Wilson fermions in the fixed scale approach. A 2+1 flavor stout and clover improved action is used at four lattice spacings allowing for control over discretization errors. The light quark masses in this first study are fixed to heavier than physical values. The renormalized chiral condensate, quark number susceptibility and the Polyakov loop is measured and the results are compared with the staggered formulation in the fixed N_t approach. The Wilson results at the finest lattice spacing agree with the staggered results at the highest N_t.Comment: 7 pages, Talk presented at the XXIX International Symposium on Lattice Field Theory (Lattice 2011), July 10-16, 2011, Squaw Valley, Lake Tahoe, California, US

Evaluation of Machine Learning Algorithms for Object-Based Mapping of Landslide Zones Using UAV Data

Landslides are a critical geological phenomenon with devastating and catastrophic consequences. With the recent advancements in the geoinformation domain, landslide documentation and inventorization can be achieved with automated workflows using aerial platforms such as unmanned aerial vehicles (UAVs). As a result, ultra-high-resolution datasets are available for analysis at low operational costs. In this study, different segmentation and classification approaches were utilized for object-based landslide mapping. An integrated object-based image analysis (OBIA) workflow is presented incorporating orthophotomosaics and digital surface models (DSMs) with expert-based and machine learning (ML) algorithms. For segmentation, trial and error tests and the Estimation of Scale Parameter 2 (ESP 2) tool were implemented for the evaluation of different scale parameters. For classification, machine learning algorithms (K-Nearest Neighbor, Decision Tree, and Random Forest) were assessed with the inclusion of spectral, spatial, and contextual characteristics. For the ML classification of landslide zones, 60% of the reference segments have been used for training and 40% for validation of the models. The quality metrics of Precision, Recall, and F1 were implemented to evaluate the models’ performance under the different segmentation configurations. Results highlight higher performances for landslide mapping when DSM information was integrated. Hence, the configuration of spectral and DSM layers with the RF classifier resulted in the highest classification agreement with an F1 value of 0.85

Docker-Based Evaluation Framework for Video Streaming QoE in Broadband Networks

Video streaming is one of the top traffic contributors in the Internet and a frequent research subject. It is expected that streaming traffic will grow 4-fold for video globally and 9-fold for mobile video between 2017 and 2022. In this paper, we present an automatized measurement framework for evaluating video streaming QoE in operational broadband networks, using headless streaming with a Docker-based client, and a server-side implementation allowing for the use of multiple video players and adaptation algorithms. Our framework allows for integration with the acsMONROE testbed and Bitmovin Analytics, which bring on the possibility to conduct large-scale measurements in different networks, including mobility scenarios, and monitor different parameters in the application, transport, network, and physical layers in real-time

Object‐based image analysis for detecting indicators of mine presence to support suspected hazardous area re‐delineation

In the framework of Mine Action, the extent of Suspected Hazardous Areas (SHAs) is often overestimated. This study investigates the potential of Object‐Based Image Analysis (OBIA) for extracting Indicators of Mine Presence (IMP) to support a more precise delineation of SHAs, with the aim of ensuring an optimal use of demining resources. The study area is situated in the Svilaja mountain range in Croatia. Using 3K colour aerial photographs, we implemented two approaches for the extraction of dry stone walls located in an area that displays traces of military activities. The first approach uses object‐based class modelling, which describes an iterative process of segmentation and classification. The second approach implements supervised learning techniques based on advanced statistical classification methods, i.e. Support Vector Machines, Random Forests and Recursive Partitioning. The results are compared, the strengths and limitations of both approaches are discussed, and perspectives for further improvements are considered.info:eu-repo/semantics/publishe

Advanced General Survey Tools Description: TIRAMISU deliverable D.210.1

Description des outils développés en support à l'Enquête Générale, dans le cadre de la lutte contre les mines (déminage humanitaire).info:eu-repo/semantics/nonPublishe

Non-Technical Survey Tool Description: TIRAMISU deliverable D.220.1

Description des outils développés en support à l'Enquête Non-Technique dans le cadre de la lutte contre les mines (déminage humanitaire).info:eu-repo/semantics/nonPublishe